Bernhard SchölkopfHow Can We Use Machine Learning in the Search for Exoplanets?

Bernhard Schölkopf
How Can We Use Machine Learning in the Search for Exoplanets?

Bernhard Schölkopf is Director of the Max Planck Institute for Intelligent Systems in Tübingen and the head of the Department for Empirical Inference. He studied physics, mathematics, and philosophy in both Tübingen (Germany) and London. Since 2002 he is an Honorary Professor at Technical University Berlin.
Schölkopf’s main research interest concerns inference from empirical data and machine learning. He applies his research on machine learning to the exploration of exoplanets, that is planets beyond our solar system.

"The Max Planck Society is Germany's most successful research organization. Since its establishment in 1948, no fewer than 18 Nobel laureates have emerged from the ranks of its scientists, putting it on a par with the best and most prestigious research institutions worldwide. The more than 15,000 publications each year in internationally renowned scientific journals are proof of the outstanding research work conducted at Max Planck Institutes – and many of those articles are among the most-cited publications in the relevant field." (Source)

Institute

"Our goal is to understand the principles of perception, learning and action in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems. The Institute studies these principles in biological, computational, hybrid, and material systems ranging from nano to macro scales. We take a highly interdisciplinary approach that combines mathematics, computation, material science, and biology." (Source)

Map

Exoplanets are planets beyond our own solar system. Since they do not emit much light and moreover are very close to their parent stars they are difficult to detect directly. When searching for exoplanets, astronomers use telescopes to monitor the brightness of the parent star under investigation: Changes in brightness can point to a passing planet that obstructs part of the star’s surface. The recorded signal, however, contains not only the physical signal of the star but also systematic errors caused by the instrument. As BERNHARD SCHÖLKOPF explains in this video, this noise can be removed by comparing the signal of the star of interest to those of a large number of other stars. Commonalities in their signals might be due to confounding effects of the instrument. Using machine learning, these observations can be used to train a system to predict the errors and correct the light curves.

Latest Thinking uses cookies to ensure that we give you the best experience on our website. To make this site work properly, we sometimes place small data files called cookies on your device. Most big websites do this. If you continue, we assume that you consent to receive cookies from our website.